A multi-objective evolutionary algorithm for color image segmentation

Abstract

In this paper, we present a multi-objective segmentation approach for color images. Three objectives, overall deviation, edge value, and connectivity measure, are optimized simultaneously using a multi-objective evolutionary algorithm (MOEA). To demonstrate the effectiveness of the proposed approach, experiments are conducted on benchmark images. The results justify that the proposed approach is able to partition color images in a number of segments consistent with human visual perception. For quantitative evaluation, we extend the existing Probabilistic Rand Index (PRI) considering multi-objective segmentation. The outcomes show that the proposed approach can obtain non-dominated and near-optimal segment solutions satisfying several criteria simultaneously. It can also find the correct number of segments automatically.